Gartner Research

Hype Cycle for Data Science and Machine Learning, 2020

Summary

Organizations are industrializing their DSML initiatives through increased automation and improved access to ML artefacts, and by accelerating the journey from proof of concept to production. Data and analytics leaders should use this report to understand key trends and innovations.

Published: 28 July 2020

ID: G00450404

Analyst(s): Shubhangi Vashisth Alexander Linden Jim Hare Pieter den Hamer

Table Of Contents

Analysis

  • What You Need to Know
  • The Hype Cycle
  • The Priority Matrix
  • Off the Hype Cycle
  • On the Rise
    • Quantum ML
    • Self-Supervised Learning
    • Generative Adversarial Networks
    • Differential Privacy
    • Federated Machine Learning
    • Adaptive ML
    • Kubeflow
    • Reinforcement Learning
    • Transfer Learning
    • Synthetic Data
  • At the Peak
    • Decision Intelligence
    • Large-Scale Pretrained Language Model
    • AI-Related C&SI Services
    • Data Labeling and Annotation Services
    • Explainable AI
    • MLOps
    • Augmented DSML
    • AutoML
    • Citizen Data Science
    • Deep Neural Networks (Deep Learning)
    • Prescriptive Analytics
  • Sliding Into the Trough
    • Graph Analytics
    • Advanced Video/Image Analytics
    • Event Stream Processing
  • Climbing the Slope
    • Predictive Analytics
    • Text Analytics
  • Entering the Plateau
    • Apache Spark
    • Notebooks
  • Appendixes
    • Hype Cycle Phases, Benefit Ratings and Maturity Levels

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